A Nearest Trajectory Strategy for Time Series Prediction
نویسنده
چکیده
A method of local modeling for predicting time series generated by nonlinear dynamic systems is proposed that incorporates a weighted Euclidean metric and a novel -steps ahead crossvalidation error to assess model accuracy. The tradeo between the cost of computation and model accuracy is discussed in the context of optimizing model parameters. A fast nearest neighbor algorithm and a novel modi cation to nd neighboring trajectory segments are described.
منابع مشابه
A Novel Fuzzy Based Method for Heart Rate Variability Prediction
Abstract In this paper, a novel technique based on fuzzy method is presented for chaotic nonlinear time series prediction. Fuzzy approach with the gradient learning algorithm and methods constitutes the main components of this method. This learning process in this method is similar to conventional gradient descent learning process, except that the input patterns and parameters are stored in mem...
متن کاملChaotic Analysis and Prediction of River Flows
Analyses and investigations on river flow behavior are major issues in design, operation and studies related to water engineering. Thus, recently the application of chaos theory and new techniques, such as chaos theory, has been considered in hydrology and water resources due to relevant innovations and ability. This paper compares the performance of chaos theory with Anfis model and discusses ...
متن کاملMethodology for long-term prediction of time series
In this paper, a global methodology for the long-term prediction of time series is proposed. This methodology combines direct prediction strategy and sophisticated input selection criteria: k-nearest neighbors approximation method (k-NN), mutual information (MI) and nonparametric noise estimation (NNE). A global input selection strategy that combines forward selection, backward elimination (or ...
متن کاملTabu Search with Delta Test for Time Series Prediction using OP-KNN
This paper presents a working combination of input selection strategy and a fast approximator for time series prediction. The input selection is performed using Tabu Search with the Delta Test. The approximation methodology is called Optimally-Pruned k -Nearest Neighbors (OP-KNN), which has been recently developed for fast and accurate regression and classification tasks. In this paper we demon...
متن کاملMutual Information and k-Nearest Neighbors Approximator for Time Series Prediction
This paper presents a method that combines Mutual Information and k-Nearest Neighbors approximator for time series prediction. Mutual Information is used for input selection. K-Nearest Neighbors approximator is used to improve the input selection and to provide a simple but accurate prediction method. Due to its simplicity the method is repeated to build a large number of models that are used f...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1998